settings = _loader.settings_loader(name, skylab_bins=use_skylab_bins)[name] llh_model = EnergyLLH(**settings["llh_model_opts"]) llh = PointSourceLLH(exp, mc, livetime, llh_model, scramble=True, **settings["llh_opts"]) multillh.add_sample(name, llh) del exp del mc gc.collect() # Do background only trials print(":: Starting {} background trials ::".format(ntrials)) _t0 = time() trials = multillh.do_trials(ntrials, src_ra=ra, src_dec=dec, src_w=w) print(":: Done. {} ::".format(sec2str(time() - _t0))) # Save trials out = { "ts": trials["TS"].tolist(), "ns": trials["nsources"].tolist(), "gamma": trials["gamma"].tolist(), "rnd_seed": rnd_seed, "ntrials": ntrials, } _fname = os.path.join(outpath, "job_{}.json.gz".format(job_id)) with gzip.open(_fname, "w") as fp: json.dump(out, fp=fp, indent=2) print("Saved to:\n {}".format(_fname))
elif years == 1: print("Using IC86") llh = datascript.init86I(mode='box') if catalog: if years == 1: bckg_trials = PointSourceLLH.do_trials( llh[0], n_iter=batchsize, src_ra=src_ra, src_dec=src_dec, src_w=modelweights['{}'.format(llhweight)]) else: bckg_trials = MultiPointSourceLLH.do_trials( llh[0], n_iter=batchsize, src_ra=src_ra, src_dec=src_dec, src_w=modelweights['{}'.format(llhweight)]) else: if years == 1: bckg_trials = PointSourceLLH.do_trials(llh[0], n_iter=batchsize, src_ra=src_ra, src_dec=src_dec) else: bckg_trials = MultiPointSourceLLH.do_trials(llh[0], n_iter=batchsize, src_ra=src_ra, src_dec=src_dec) #choose an output dir, and make sure it exists